PLT用法

elastic search 的python测试运行(Python test run of elastic search)

Python 数据结构，英文版Python 数据结构，英文版Python 数据结构，英文版(Python data structure is introduced, the English versionPython data structure is introduced, the English versionPython data structure is introduced, the English versionPython data structure is introduced, the English version)

用python写的人脸识别库，可以进行人脸检测、跟踪和识别，也可以训练自己的类刚体识别器。稍作扩展，可以做基本表情识别。                                                                                    

说明：  蒙特卡洛编程，含程序代码，代码直接复制即可，还有实例！！！(Monte Carlo programming, including program code.)

卡尔曼滤波的一个典型实例是从一组有限的，包含噪声的，对物体位置的观察序列（可能有偏差）预测出物体的位置的坐标及速度。在很多工程应用(如雷达、计算机视觉)中都可以找到它的身影。同时，卡尔曼滤波也是控制理论以及控制系统工程中的一个重要课题。(A typical example of the Kalman filter, from a limited set, containing the noise, the position of the object observed sequence (there may be deviation) to predict the coordinates of the location and speed of the object. Its presence can be found in many engineering applications (such as radar, computer vision). Meanwhile, the Kalman filter is control theory, and control is an important topic in systems engineering.)

实现深度学习中，对图像的注意力机制，强化网络的学习能力，提高网络精度和泛化能力。(It can improve the classification accuracy of deep learning network, and can be easily embedded into other networks such as densenet,resnet and etc.)

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说明：  用N来表示训练用例（样本）的个数，M表示特征数目。 输入特征数目m，用于确定决策树上一个节点的决策结果；其中m应远小于M。 从N个训练用例（样本）中以有放回抽样的方式，取样N次，形成一个训练集（即bootstrap取样），并用未抽到的用例（样本）作预测，评估其误差。 对于每一个节点，随机选择m个特征，决策树上每个节点的决定都是基于这些特征确定的。根据这m个特征，计算其最佳的分裂方式。 每棵树都会完整成长而不会剪枝，这有可能在建完一棵正常树状分类器后会被采用）。(N is used to represent the number of training cases (samples), and M is used to represent the number of features. The number of input features m is used to determine the decision result of a node in the decision tree, where m should be far less than m. From N training cases (samples), n times are sampled in the way of put back sampling to form a training set (i.e. bootstrap sampling), and the unselected cases (samples) are used to predict and evaluate the error. For each node, m features are randomly selected, and the decision of each node in the decision tree is determined based on these features. According to these m characteristics, the best splitting mode is calculated. Each tree will grow completely without pruning, which may be adopted after building a normal tree classifier).)

说明：  利用相似三角形原理来使用单目摄像头测距，使用的语言为python，测试图形为圆形。(Using the principle of similar triangle to use monocular camera for ranging, using Python language, the test figure is circular.)